Tong Zhou
Air China Cargo Co., Ltd., Beijing 101318, China
Guojun Zhang
Quadrant International Inc., San Diego, CA 92121, USA
Yiqun Cai
University of Florida, Herbert Wertheim College, Gainesville, FL 32608, USA
Bearings are crucial components in aircraft power systems and mechanical structures, and their complex fault characteristics significantly impact flight safety. To improve the accuracy of aircraft bearing fault diagnosis, this paper proposes a novel diagnostic method based on optimized Variational Mode Decomposition (VMD) and Generalized Multi-Scale Fuzzy Entropy (GMFE). First, the Moth-Flame Optimization (MFO) algorithm is used to optimize the two parameters of the VMD signal decomposition method—the number of modes and the penalty factor —to obtain the optimal parameter combination . This optimized VMD method is then applied for signal decomposition and reconstruction of bearing vibration signals. Next, the GMFE entropy algorithm is employed to extract fault features from the reconstructed signals, resulting in the required set of bearing fault feature vectors. Finally, the extracted feature vector set is input into a Support Vector Machine (SVM) model for classification and diagnosis of aircraft bearing faults. Experimental results indicate that the proposed method effectively enhances the identification accuracy of bearing diagnosis and demonstrates excellent fault feature extraction capabilities.
[1] ALMASI, A., 2010. A new study and model for the mechanism of process reciprocating compressors and pumps. Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering. 224(2), 143–147.
[2] Rejith, R., Kesavan, D., Chakravarthy, P., et al., 2023. Bearings for aerospace applications. Tribology International. 181, 108312.
[3] Kumar, N., Satapathy, R.K., 2023. Bearings in aerospace, application, distress, and life: a review. Journal of Failure Analysis and Prevention. 23(3), 915–947.
[4] Dai, W., 2023. Design of traffic improvement plan for line 1 Baijiahu station of Nanjing metro. Innovations in Applied Engineering and Technology. 1–11. DOI: https://doi.org/10.58195/iaet.v2i1.133
[5] Dai, W., 2022. Evaluation and improvement of carrying capacity of a traffic system. Innovations in Applied Engineering and Technology. 1–9. DOI: https://doi.org/10.58195/iaet.v1i1.001
[6] Ren, P., Zhao, Z., Yang, Q., 2023. Exploring the Path of Transformation and Development for Study Abroad Consultancy Firms in China. Journal of Computational Methods in Engineering Applications. 1–12.
[7] Akansu, A.N., Liu, Y., 1991. On-signal decomposition techniques. Optical Engineering. 30(7), 912–920.
[8] Civera, M., Surace, C., 2021. A comparative analysis of signal decomposition techniques for structural health monitoring on an experimental benchmark. Sensors. 21(5), 1825.
[9] Zhao, Z., Ren, P., Yang, Q., 2023. Student Self-Management, Academic Achievement: Exploring the Mediating Role of Self-Efficacy and the Moderating Influence of Gender—Insights From a Survey Conducted in 3 Universities in America. Journal of Integrated Social Sciences and Humanities. 1, 1–12.
[10] Lei, J., 2022. Green Supply Chain Management Optimization Based on Chemical Industrial Clusters. Innovations in Applied Engineering and Technology. 1–17.
[11] Dai, W., 2021. Safety evaluation of traffic system with historical data based on Markov process and deep-reinforcement learning. Journal of Computational Methods in Engineering Applications. 1–14.
[12] Dragomiretskiy, K., Zosso, D., 2014. Variational mode decomposition. IEEE Transactions on Signal Processing. 62(3), 531–544.
[13] Xi, T., Ge, Z.Y., Wang, L.J., 2022. Fault Diagnosis of Rolling Bearing Based on Parameter Optimization of VMD and DBN. Modular Machine Tool & Automatic Manufacturing Technique. (12), 57–61.
[14] Li, H., Li, D.W., Zhu, H.Q., et al., 2021. An optimized VMD algorithm and its application in speech signal denoising. Journal of Jilin University(Science Edition). 59(05), 1219–1227.
[15] Zhang, P., Zhang, W.H., Zhao, X.H., et al., 2021. Application of WOA-VMD Algorithm in Bearing Fault Diagnosis. Noise and Vibration Control. 41(04), 86–93+275.
[16] Pincus, S.M., 1991. Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences. 88, 2297–2301.
[17] Richman, J.S., Moorman, J.R., 2000. Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology. 2000, 278(6), H2039–H2049.
[18] Chen, W., Wang, Z., Xie, H., et al., 2007. Characterization of surface EMG signal based on fuzzy entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 15, 266–272.
[19] Costa, M., Goldberger, A.L., Peng, C.K., 2005. Multiscale entropy analysis of biological signals. Physical Review E. 71, 1–18.
[20] Lei, J., 2022. Efficient Strategies on Supply Chain Network Optimization for Industrial Carbon Emission Reduction. Journal of Computational Methods in Engineering Applications. 1–11.
[21] Lei, J., Nisar, A., 2023. Investigating the Influence of Green Technology Innovations on Energy Consumption and Corporate Value: Empirical Evidence from Chemical Industries of China. Innovations in Applied Engineering and Technology. 1–16.
[22] Xiong, S., Zhang, H., Wang, M., et al., 2022. Distributed Data Parallel Acceleration-Based Generative Adversarial Network for Fingerprint Generation. Innovations in Applied Engineering and Technology. 1–12.
[23] ur Rehman, N., Aftab, H., 2019. Multivariate variational mode decomposition. IEEE Transactions on signal processing. 67(23), 6039–6052.
[24] Lian, J., Liu, Z., Wang, H., et al., 2018. Adaptive variational mode decomposition method for signal processing based on mode characteristic. Mechanical Systems and Signal Processing. 107, 53–77.
[25] Nazari, M., Sakhaei, S.M., 2020. Successive variational mode decomposition. Signal Processing. 174, 107610.
[26] Mirjalili, S., 2015. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems. 89, 228–249.
[27] Xiong, S., Chen, X., Zhang, H., 2023. Deep Learning-Based Multifunctional End-to-End Model for Optical Character Classification and Denoising. Journal of Computational Methods in Engineering Applications. 1–13.
[28] Yu, L., Li, J., Cheng, S., et al., 2013. Secure continuous aggregation in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems. 25(3), 762–774.
[29] Chen, X., Zhang, H., 2023. Performance Enhancement of AlGaN-based Deep Ultraviolet Light-emitting Diodes with AlxGa1-xN Linear Descending Layers. Innovations in Applied Engineering and Technology. 1–10.
[30] Zhou, Z., Wu, J., Cao, Z., et al., 2021. On-Demand Trajectory Prediction Based on Adaptive Interaction Car Following Model with Decreasing Tolerance. In Proceedings of the 2021 International Conference on Computers and Automation (CompAuto); Paris, France, 7–9 September 2021. pp. 67–72.
[31] Xiong, S., Yu, L., Shen, H., et al., 2012. Efficient algorithms for sensor deployment and routing in sensor networks for network-structured environment monitoring. In Proceedings of the 2012 Proceedings IEEE INFOCOM; Orlando, FL, USA, 25–30 March 2012. pp. 1008–1016.
[32] Feng, Z., Xiong, S., Cao, D., et al., 2015. Hrs: A hybrid framework for malware detection. In Proceedings of the 2015 ACM International Workshop on International Workshop on Security and Privacy Analytics; San Antonio, TX, USA, 4 March 2015. pp. 19–26.
[33] Xiong, S., Li, J., Li, M., et al., 2011. Multiple task scheduling for low-duty-cycled wireless sensor networks. In Proceedings of the 2011 Proceedings IEEE INFOCOM; Shanghai, China, 10–15 April 2011. pp. 1323–1331.
[34] Yu, L., Li, J., Cheng, S., et al., 2011. Secure continuous aggregation via sampling-based verification in wireless sensor networks. In Proceedings of the 2011 Proceedings IEEE INFOCOM. IEEE; Shanghai, China, 10–15 April 2011. pp. 1763–1771.
[35] Xiong, S., Zhang, H., Wang, M., 2022. Ensemble Model of Attention Mechanism-Based DCGAN and Autoencoder for Noised OCR Classification. Journal of Electronic & Information Systems. 4(1), 33–41.
[36] Li, Y., Wang, J.D., Zhao, H.Y., et al., 2019. A Fault Diagnosis Method for Reciprocating Compressor Bearings Based on Parameter Optimization VMD and MDE. Modular Machine Tool & Automatic Manufacturing Technique. (04), 120–123+132.
[37] Tang, Z.J., Sun, R.F., 2021. Fault Diagnosis of Fan Bearing Based on Multiscale Fuzzy Entropy and STOA-SVM. Electric Machines & Control Application. 48(12), 66–70.
[38] Li, C.Z., Zheng, J.D., Pan, H.Y., et al., 2019. Fault Diagnosis Method of Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Support Vector Machine. China Mechanical Engineering. 30(14), 1713–1719.
[39] Hsu, C., Lin, C.J., 2002. A Comparison on Methods for Multi-class Support Vector Machines. IEEE Transactions on Neural Networks. 13(2), 415–442.
[40] Li, J., Xiong, S., 2010. Efficient Pr-skyline query processing and optimization in wireless sensor networks. Wireless Sensor Network. 2(11), 838.
[41] Xiong, S., Li, J., 2010. An efficient algorithm for cut vertex detection in wireless sensor networks. In Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems; Genoa, Italy, 21–25 June 2010. pp. 368–377.
[42] Wang, H., Li, J., Xiong, S., 2008. Efficient join algorithms for distributed information integration based on XML. International Journal of Business Process Integration and Management. 3(4), 271–281.
Copyright © 2024 Tong Zhou, Guojun Zhang, Yiqun Cai
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
